DEMpapers.bib

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@PHDTHESIS{Beal2003,
  author = {Matthew J. Beal},
  title = {Variational algorithms for approximate {B}ayesian inference},
  school = {University of London},
  year = {2003},
  file = {Beal2003.pdf:Beal2003.pdf:PDF},
  owner = {sbitzer},
  timestamp = {2012.11.18}
}

@INCOLLECTION{Doucet2011,
  author = {Doucet, Arnaud and Johansen, Adam M.},
  title = {A Tutorial on Particle Filtering and Smoothing: Fifteen years later},
  booktitle = {Oxford Handbook of Nonlinear Filtering},
  publisher = {Oxford University Press},
  year = {2011},
  abstract = {Optimal estimation problems for non-linear non-Gaussian state-space
	models do not typically admit analytic solutions. Since their introduction
	in 1993, particle filtering methods have become a very popular class
	of algorithms to solve these estimation problems numerically in an
	online manner, i.e. recursively as observations become available,
	and are now routinely used in fields as diverse as computer vision,
	econometrics, robotics and navigation. The objective of this tutorial
	is to provide a complete, up-to-date survey of this field as of 2008.
	Basic and advanced particle methods for filtering as well as smoothing
	are presented.},
  file = {Doucet2011.pdf:Doucet2011.pdf:PDF},
  owner = {bitzer},
  timestamp = {2012.07.03}
}

@ARTICLE{Friston2008,
  author = {Karl Friston},
  title = {Hierarchical models in the brain.},
  journal = {PLoS Comput Biol},
  year = {2008},
  volume = {4},
  pages = {e1000211},
  number = {11},
  month = {Nov},
  abstract = {This paper describes a general model that subsumes many parametric
	models for continuous data. The model comprises hidden layers of
	state-space or dynamic causal models, arranged so that the output
	of one provides input to another. The ensuing hierarchy furnishes
	a model for many types of data, of arbitrary complexity. Special
	cases range from the general linear model for static data to generalised
	convolution models, with system noise, for nonlinear time-series
	analysis. Crucially, all of these models can be inverted using exactly
	the same scheme, namely, dynamic expectation maximization. This means
	that a single model and optimisation scheme can be used to invert
	a wide range of models. We present the model and a brief review of
	its inversion to disclose the relationships among, apparently, diverse
	generative models of empirical data. We then show that this inversion
	can be formulated as a simple neural network and may provide a useful
	metaphor for inference and learning in the brain.},
  doi = {10.1371/journal.pcbi.1000211},
  file = {Friston2008.pdf:Friston2008.pdf:PDF},
  institution = {The Wellcome Trust Centre of Neuroimaging, University College London,
	London, United Kingdom. k.friston@fil.ion.ucl.ac.uk},
  keywords = {Algorithms; Animals; Brain, anatomy /&/ histology/physiology; Humans;
	Linear Models; Mental Processes, physiology; Models, Neurological;
	Nerve Net, anatomy /&/ histology/physiology; Neural Networks (Computer);
	Nonlinear Dynamics; Probability},
  language = {eng},
  medline-pst = {ppublish},
  owner = {bitzer},
  pmid = {18989391},
  timestamp = {2010.04.12},
  url = {http://dx.doi.org/10.1371/journal.pcbi.1000211}
}

@ARTICLE{Friston2008a,
  author = {K.J. Friston and N. Trujillo-Barreto and J. Daunizeau},
  title = {{DEM}: A variational treatment of dynamic systems},
  journal = {NeuroImage},
  year = {2008},
  volume = {41},
  pages = {849 - 885},
  number = {3},
  abstract = {This paper presents a variational treatment of dynamic models that
	furnishes time-dependent conditional densities on the path or trajectory
	of a system's states and the time-independent densities of its parameters.
	These are obtained by maximising a variational action with respect
	to conditional densities, under a fixed-form assumption about their
	form. The action or path-integral of free-energy represents a lower
	bound on the model's log-evidence or marginal likelihood required
	for model selection and averaging. This approach rests on formulating
	the optimisation dynamically, in generalised coordinates of motion.
	The resulting scheme can be used for online Bayesian inversion of
	nonlinear dynamic causal models and is shown to outperform existing
	approaches, such as Kalman and particle filtering. Furthermore, it
	provides for dual and triple inferences on a system's states, parameters
	and hyperparameters using exactly the same principles. We refer to
	this approach as dynamic expectation maximisation (DEM).},
  doi = {10.1016/j.neuroimage.2008.02.054},
  file = {Friston2008a.pdf:Friston2008a.pdf:PDF},
  issn = {1053-8119},
  keywords = {Variational Bayes; Free energy; Action; Dynamic expectation maximisation;
	Dynamical systems; Nonlinear; Bayesian filtering; Variational filtering},
  owner = {bitzer},
  timestamp = {2010.04.12},
  url = {http://www.sciencedirect.com/science/article/B6WNP-4S19RH8-1/2/48c1aa6f77adaeeaba06cd57573a986d}
}

@BOOK{Murphy2012,
  title = {Machine Learning: A Probabilistic Perspective},
  publisher = {MIT Press},
  year = {2012},
  author = {Kevin P. Murphy},
  series = {Adaptive Computation and Machine Learning},
  abstract = {My book (MLaPP) is similar to Bishop's Pattern recognition and machine
	learning, Hastie et al's The Elements of Statistical Learning, and
	to Wasserman's All of statistics, with the following key differences:
	
	
	 MLaPP is more accessible to undergrads. It pre-supposes a background
	in probability, linear algebra, calculus, and programming; however,
	the mathematical level ramps up slowly, with more difficult sections
	clearly denoted as such. This makes the book suitable for both undergrads
	and grads. Summaries of the relevant mathematical background, on
	topics such as linear algebra, optimization and classical statistics
	make the book self-contained. 
	
	
	 MLaPP is more practically-oriented. In particular, it comes with
	Matlab software to reproduce almost every figure, and to implement
	almost every algorithm, discussed in the book. It includes many worked
	examples of the methods applied to real data, with readable source
	code online. 
	
	
	 MLaPP covers various important topics that are not discussed in these
	other books, such as conditional random fields, deep learning, etc.
	
	
	
	 MLaPP is "more Bayesian" than the Hastie or Wasserman books, but
	"more frequentist" than the Bishop book. In particular, in MLaPP,
	we make extensive use of MAP estimation, which we regard as "poor
	man's Bayes". We prefer this to the regularization interpretation
	of MAP, because then all the methods in the book (except cross validation...)
	can be viewed as probabilistic inference, or some approximation thereof.
	The MAP interpretation also allows for an easy "upgrade path" to
	more accurate methods of approximate Bayesian inference, such as
	empirical Bayes, variational Bayes, MCMC, SMC, etc. 
	
	
	 The emphasis is on simple parametric models (linear and logistic
	regression, discriminant analysis/ naive Bayes, mixture models, factor
	analysis, graphical models, etc.), which are the ones most often
	used in practice. However, we also briefly discuss non-parametric
	models, such as Gaussian processes, Dirichlet processes, SVMs, RVMs,
	etc.},
  owner = {bitzer},
  timestamp = {2012.11.19},
  url = {http://www.cs.ubc.ca/~murphyk/MLbook/index.html}
}

@TECHREPORT{Petersen2008,
  author = {K. B. Petersen and M. S. Pedersen},
  title = {The Matrix Cookbook},
  institution = {Technical University of Denmark},
  year = {2008},
  month = {oct},
  note = {Version 20081110},
  abstract = {Matrix identities, relations and approximations. A desktop reference
	for quick overview of mathematics of matrices.},
  file = {Petersen2008.pdf:Petersen2008.pdf:PDF},
  keywords = {Matrix identity, matrix relations, inverse, matrix derivative},
  owner = {bitzer},
  publisher = {Technical University of Denmark},
  timestamp = {2010.07.30},
  url = {http://www2.imm.dtu.dk/pubdb/p.php?3274}
}

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